IDEAS home Printed from https://ideas.repec.org/a/hin/complx/4708075.html
   My bibliography  Save this article

Hybrid Online and Offline Reinforcement Learning for Tibetan Jiu Chess

Author

Listed:
  • Xiali Li
  • Zhengyu Lv
  • Licheng Wu
  • Yue Zhao
  • Xiaona Xu

Abstract

In this study, hybrid state-action-reward-state-action (SARSA ) and Q-learning algorithms are applied to different stages of an upper confidence bound applied to tree search for Tibetan Jiu chess. Q-learning is also used to update all the nodes on the search path when each game ends. A learning strategy that uses SARSA and Q-learning algorithms combining domain knowledge for a feedback function for layout and battle stages is proposed. An improved deep neural network based on ResNet18 is used for self-play training. Experimental results show that hybrid online and offline reinforcement learning with a deep neural network can improve the game program’s learning efficiency and understanding ability for Tibetan Jiu chess.

Suggested Citation

  • Xiali Li & Zhengyu Lv & Licheng Wu & Yue Zhao & Xiaona Xu, 2020. "Hybrid Online and Offline Reinforcement Learning for Tibetan Jiu Chess," Complexity, Hindawi, vol. 2020, pages 1-11, May.
    • Handle: RePEc:hin:complx:4708075
    DOI: 10.1155/2020/4708075
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2020/4708075.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2020/4708075.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2020/4708075?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:4708075. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.